A genetic algorithm for the design of a fuzzy controller for active queue management

Active queue management (AQM) policies are those policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the hosts on the network borders, and the adoption of a suitable control policy. This paper proposes the adoption of a fuzzy proportional integral (FPI) controller as an active queue manager for Internet routers. The analytical design of the proposed FPI controller is carried out in analogy with a proportional integral (PI) controller, which recently has been proposed for AQM. A genetic algorithm is proposed for tuning of the FPI controller parameters with respect to optimal disturbance rejection. In the paper the FPI controller design metodology is described and the results of the comparison with random early detection (RED), tail drop, and PI controller are presented

An agent-based fuzzy cognitive map approach to the strategic marketing planning for industrial firms

This is the post-print version of the final paper published in Industrial Marketing Management. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Industrial marketing planning is a typical example of an unstructured decision making problem due to the large number of variables to consider and the uncertainty imposed on those variables. Although abundant studies identified barriers and facilitators of effective industrial marketing planning in practice, the literature still lacks practical tools and methods that marketing managers can use for the task. This paper applies fuzzy cognitive maps (FCM) to industrial marketing planning. In particular, agent based inference method is proposed to overcome dynamic relationships, time lags, and reusability issues of FCM evaluation. MACOM simulator also is developed to help marketing managers conduct what-if scenarios to see the impacts of possible changes on the variables defined in an FCM that represents industrial marketing planning problem. The simulator is applied to an industrial marketing planning problem for a global software service company in South Korea. This study has practical implication as it supports marketing managers for industrial marketing planning that has large number of variables and their cause–effect relationships. It also contributes to FCM theory by providing an agent based method for the inference of FCM. Finally, MACOM also provides academics in the industrial marketing management discipline with a tool for developing and pre-verifying a conceptual model based on qualitative knowledge of marketing practitioners.Ministry of Education, Science and Technology (Korea

Data Analytic Approach to Support the Activation of Special Signal Timing Plans in Response to Congestion

Improving arterial network performance has become a major challenge that is significantly influenced by signal timing control. In recent years, transportation agencies have begun focusing on Active Arterial Management Program (AAM) strategies to manage the performance of arterial streets under the flagship of Transportation Systems Management & Operations (TSM&O) initiatives. The activation of special traffic signal plans during non-recurrent events is an essential component of AAM and can provide significant benefits in managing congestion. Events such as surges in demands or lane blockages can create queue spillbacks, even during off-peak periods resulting in delays and spillbacks to upstream intersections. To address this issue, some transportation agencies have started implementing processes to change the signal timing in real time based on traffic signal engineer/expert observations of incident and traffic conditions at the intersections upstream and downstream of congested locations. This dissertation develops methods to automate and enhance such decisions made at traffic management centers. First, a method is developed to learn from experts’ decisions by utilizing a combination of Recursive Partitioning and Regression Decision Tree (RPART) and Fuzzy Rule-Based System (FRBS) to deal with the vagueness and uncertainty of human decisions. This study demonstrates the effectiveness of this method in selecting plans to reduce congestion during non-recurrent events. However, the method can only recommend the changes in green time to the movement affected by the incident and does not give an optimized solution that considers all movements. Thus, there was a need to extend the method to decide how the reduction of green times should be distributed to other movements at the intersection. Considering the above, this dissertation further develops a method to derive optimized signal timing plans during non-recurrent congestion that considers the operations of the critical direction impacted by the incident, the overall corridor, as well as the critical intersection movement performance. The prerequisite of optimizing the signal plans is the accurate measurements of traffic flow conditions and turning movement counts. It is also important to calibrate any utilized simulation and optimization models to replicate the field traffic states according to field traffic conditions and local driver behaviors. This study evaluates the identified special signal-timing plan based on both the optimization and the DT and FRBS approaches. Although the DT and FRBS model outputs are able to reduce the existing queue and improve all other performance measures, the evaluation results show that the special signal timing plan obtained from the optimization method produced better performance compared to the DT and FRBS approaches for all of the evaluated non-recurrent conditions. However, there are opportunities to combine both approaches for the best selection of signal plans

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Decision Support System for Production Planning from a Sustainability Perspective

Manufacturing enterprises supply our global demand for products, creating economic value. Moreover, they are also responsible for several environmental and social impacts, e.g., green-house gases, waste, and poor working conditions. These impacts cause climate change, air and sea pollution, and social inequality, which are a few examples of current challenges for global sustainability strategies. However, researchers have widely addressed these impacts and warned politicians and society about the risk of the collapse of ecosystems. Despite these warnings, manufacturing enterprises still have difficulties improving the sustainability of their production processes. Therefore, new technologies are required to support enterprises and help determine their production processes’ sustainability status by considering multiple aspects (economic, environmental, and social). Moreover, advice should be given on how the identified issues can be avoided, reduced, or compensated for future production activities. This research presents a fuzzy decision support system and an experimental study for sustainability-based production planning. For this approach, systematic literature reviews were made, analysing concept methods for sustainability-based production management and planning. The results show, among other things, that current methods for sustainability-production planning are focused on single aspects of sustainability (e.g., energy or waste planning). Therefore, a fuzzy decision support system was developed that simultaneously evaluates social, environmental, and economic aspects. The decision support system's model identifies the most significant opportunities to improve the production program's sustainability and gives recommendations on how to change it. The decision support system was tested and validated in an experimental study in the production planning laboratory at Emden University of Applied Sciences. The study results discuss problems, needs, and challenges affecting sustainability-based production planning. Moreover, opportunities for future research were identified based on the limitations of the experimental study.As empresas transformadoras satisfazem a procura global de produtos, criando valor económico. No entanto, também são responsáveis por vários impactos ambientais e sociais, por exemplo, gases de efeito estufa, resíduos e más condições de trabalho. Estes impactos originam alterações climáticas, poluição do ar e do mar e desigualdade social, que constituem alguns exemplos dos desafios que se colocam atualmente às estratégias globais de sustentabilidade. De notar que os investigadores têm abordado amplamente estes impactos e alertado os políticos e a sociedade sobre o risco do colapso dos ecossistemas. Apesar destes alertas, as empresas transformadoras ainda têm dificuldades em melhorar a sustentabilidade dos seus processos produtivos. Como tal, são necessárias novas tecnologias para apoiar as empresas, ajudando a caracterizar o estado de sustentabilidade dos seus processos de produção, considerando múltiplos fatores (económicos, ambientais e sociais). Além disso, devem ser dados conselhos sobre o modo como os problemas identificados podem ser evitados, reduzidos ou compensados em atividades de produção futuras. A investigação realizada contribuiu para o desenvolvimento de um sistema de apoio à decisão difuso, aplicado a um estudo de caso de planeamento da produção baseado na sustentabilidade. Para o efeito, foram conduzidas revisões sistemáticas da literatura, analisando os conceitos associados aos métodos para gestão e planeamento da produção baseado na sustentabilidade. Os resultados revelam, entre outras conclusões, que os métodos atuais para o planeamento da produção sustentável estão focados em fatores isolados de sustentabilidade (e.g., planeamento energético ou de resíduos). Perante este contexto, foi desenvolvido um sistema de apoio à decisão difuso, que avalia simultaneamente fatores sociais, ambientais e económicos. O modelo do sistema de apoio à decisão identifica as oportunidades mais significativas para melhorar a sustentabilidade do programa de produção e fornece recomendações sobre o modo como este pode ser alterado. O sistema de apoio à decisão foi testado e validado num estudo de caso simulado no laboratório de planeamento da produção na Universidade de Ciências Aplicadas de Emden. Os resultados do estudo de caso permitiram analisar os problemas, necessidades e desafios que afetam o planeamento da produção baseado na sustentabilidade. Complementarmente, foram identificadas oportunidades de investigação futuras, considerando as limitações do estudo de caso realizado